Modified Search Solutions Based ABC with Mutation Algorithm for TSP
Swarm intelligence systems are basically made up of simple agents’ population, which are interacting locally with each other and with their surroundings. The artificial bee colony algorithm (ABC) is for aging behavior based optimization algorithm. In this paper, modified version of ABC algorithm, called as ABCM, is used. In this algorithm, two equations of original ABC algorithm are modified: First is the search equation of employed bee and second is the search equation of onlooker bee. These modified search equations greatly increase the exploration and exploitation of ABCM algorithm. Also in the ABCM algorithm, mutation operator is used after the employed bee phase of the ABCM algorithm. Proposed algorithm is implemented on travelling salesman problem and compared with the original ABC algorithm and ABC with SPV algorithm. Experimental results show that the proposed algorithm performance is better than the previous versions of ABC algorithm.
Cite this Article
Umesh Gera, Aparajit Shrivastava. Modified Search Solutions Based ABC with Mutation Algorithm for TSP. Journal of Artificial Intelligence Research & Advances. 2016; 3(2): 39–43p.
Dorigo M, Di Caro G. Ant Colony Optimization: A New Meta-Heuristic. In Evolutionary Computation, CEC 99, Proceedings of the 1999 Congress on. IEEE; 1999; 2.
Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization. Techn Rep TR06. Erciyes: Erciyes Univ Press; 2005. 3. Amit Singh, Neetesh Gupta, Amit Singhal. Artificial Bee Colony Algorithm with Uniform Mutation. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), Dec 20–22, 2011. 2012; 130: 503–511p.
Song Zhang, Sanyang Liu. A Novel Artificial Bee colony Algorithm for Function Optimization. Mathematical Problems in Engineering. Hindawi Publishing Corporation; Volume 2015, Article ID 129271. 5. Nishant Pathak, Sudhanshu Tiwari.
Travelling Salesman Problem Using Bee Colony with SPV. Proceedings of the International Journal of Soft Computing and Engineering (IJSCE). Jul 2012; 02(3). 6. Manish Gupta, Govind Sharma. An Efficient Modified Artificial Bee Colony Algorithm for Job Scheduling Problem. International Journal of Soft Computing and Engineering (IJSCE). Jan 2012; 1(6).
Shraddha Saxena, Kavita Sharma, Savita Shiwani, et al. Lbest Artificial Bee Colony using Structured Swarm. Advance Computing Conference (IACC), IEEE. 2014; 1354–1360p. 8. Akay B, Karaboga D. A Modified Artificial Bee Colony Algorithm for Real-Parameter Optimization. Information Sciences. 2010. doi:10.1016/j.ins.2010.07.015. 9. Vesterstrom J, Thomsen R. A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems. In Evolutionary Computation, CEC2004. Congress on. IEEE; 2004; 2: 1980–1987p.
Karaboga D, Akay B. A Comparative
Study of Artificial Bee Colony Algorithm. Appl Math Comput. 2009; 214(1): 108–132p. 11. Zhu G, Kwong S. Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization. Appl Math Comput. 2010; 217(7): 3166–3173p.
Haijun D, Qingxian F. Bee Colony Algorithm for the Function Optimization. Science Paper Online. Aug 2008; 08: 448–456p.
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